dc.contributor.author
Mardt, Andreas
dc.contributor.author
Pasquali, Luca
dc.contributor.author
Wu, Hao
dc.contributor.author
Noé, Frank
dc.date.accessioned
2018-11-27T12:31:08Z
dc.date.available
2018-11-27T12:31:08Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/23305
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-1096
dc.format.extent
1 Seite
dc.rights.uri
https://creativecommons.org/licenses/by/4.0/
dc.subject
Machine learning
en
dc.subject
Molecular modelling
en
dc.subject
Theoretical chemistry
en
dc.subject.ddc
500 Naturwissenschaften und Mathematik::540 Chemie::541 Physikalische Chemie
dc.title
Author Correction: VAMPnets for deep learning of molecular kinetics
dc.type
Wissenschaftlicher Artikel
dcterms.bibliographicCitation.articlenumber
4443
dcterms.bibliographicCitation.doi
10.1038/s41467-018-06999-0
dcterms.bibliographicCitation.journaltitle
Nature Communications
dcterms.bibliographicCitation.volume
9
dcterms.bibliographicCitation.url
https://doi.org/10.1038/s41467-018-06999-0
refubium.affiliation
Mathematik und Informatik
refubium.affiliation.other
Institut für Mathematik / Arbeitsgruppe Computational Molecular Biology
refubium.resourceType.isindependentpub
no
dcterms.accessRights.openaire
open access
dc.relation.iscorrectionof
https://refubium.fu-berlin.de/handle/fub188/23172
dcterms.isPartOf.issn
2041-1723
